DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2312.12441v1
• Date: Sun, 29 Oct 2023 15:26:37 GMT
• Title: DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification
• Authors: Neetu Sigger, Tuan Thanh Nguyen, Gianluca Tozzi, Quoc-Tuan Vien, Sinh Van Nguyen
• Abstract summary: We propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification.
• Score: 6.521187080027966
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hyperspectral images (HSI) have become popular for analysing remotely sensed images in multiple domain like agriculture, medical. However, existing models struggle with complex relationships and characteristics of spectral-spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. Our approach involves a two-step process. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification. Then, we employ a pretrained denoising U-Net to extract intermediate hierarchical features for classification. Finally, we use a supervised transformer-based classifier to perform the HSI classification. Through comprehensive experiments on HSI datasets, we evaluate the classification performance of DiffSpectralNet. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance.

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